This is our week 3 progress report. We are updating our data visualizations using ggplots. We are zoning in on the social determinants of health and the health outcomes that we will be analyzing.
We have selected the follow outcomes of health to analyze:
drug_agents_ggplot <- ggplot(data = world) +
geom_sf(data = states, color= "grey60", fill= "ivory1") +
geom_sf(data = counties, fill = NA) +
geom_sf(data= va.drug, aes(fill=Rate_Drug_Overdose_Deaths, text= NAME))+
scale_fill_viridis_c(trans= "sqrt", alpha= .4, direction= -1) +
labs(fill= "Mortality Rate
Per 100,000 People")+
geom_sf(data = agents_sf, aes( color = "red")) +
guides(color= guide_legend(title= "Agent Sites")) +
coord_sf(xlim = c(-84, -75), ylim = c(36, 40), expand = FALSE) +
xlab("Longitude") + ylab("Latitude") +
ggtitle("VCE FCS Agent Sites and Drug Overdose Mortality Rates of 2023") +
theme(panel.grid.major = element_line(color = gray(0.5), linetype = "dashed", size = 0.5),
panel.background = element_rect(fill = "azure1"))
drug_agents_ggplot
#plotly <- ggplotly(drug_agents_ggplot)
#plotly
Data by drug class are not mutually exclusive. Death can involve more than one drug, so deaths may be included in multiple graphs if more than one drug or class was involved in the death.
## Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
## : Ignoring unknown aesthetics: text
## Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
## : Ignoring unknown aesthetics: text